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 "cells": [
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    "Parameter Heatmap\n",
    "==========\n",
    "\n",
    "This tutorial will show how to optimize strategies with multiple parameters and how to examine and reason about optimization results.\n",
    "It is assumed you're already familiar with\n",
    "[basic _backtesting.py_ usage](https://kernc.github.io/backtesting.py/doc/examples/Quick%20Start%20User%20Guide.html).\n",
    "\n",
    "First, let's again import our helper moving average function.\n",
    "In practice, one should use functions from an indicator library, such as\n",
    "[TA-Lib](https://github.com/mrjbq7/ta-lib) or\n",
    "[Tulipy](https://tulipindicators.org)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jk/PycharmProjects/backtesting/venv/lib/python3.11/site-packages/backtesting/_plotting.py:50: UserWarning: Jupyter Notebook detected. Setting Bokeh output to notebook. This may not work in Jupyter clients without JavaScript support (e.g. PyCharm, Spyder IDE). Reset with `backtesting.set_bokeh_output(notebook=False)`.\n",
      "  warnings.warn('Jupyter Notebook detected. '\n"
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       "            height: 20px;\n",
       "            background-image: url();\n",
       "        }\n",
       "    </style>\n",
       "    <div>\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
       "        <span id=\"c1d9e215-ea64-4085-976e-44c7f1e81bdd\">Loading BokehJS ...</span>\n",
       "    </div>\n"
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       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  const force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "const JS_MIME_TYPE = 'application/javascript';\n",
       "  const HTML_MIME_TYPE = 'text/html';\n",
       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    const script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
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       "\n",
       "  /**\n",
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       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
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       "      const view = Bokeh.index.get_by_id(id)\n",
       "      if (view != null) {\n",
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       "        Bokeh.index.delete(view)\n",
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       "\n",
       "    const cell = handle.cell;\n",
       "\n",
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       "      drop(id)\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd_clean, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            const id = msg.content.text.trim()\n",
       "            drop(id)\n",
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       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd_destroy);\n",
       "    }\n",
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       "\n",
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       "    const output_area = handle.output_area;\n",
       "    const output = handle.output;\n",
       "\n",
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       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
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       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
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       "      const bk_div = document.createElement(\"div\");\n",
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       "      const script_attrs = bk_div.children[0].attributes;\n",
       "      for (let i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
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       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
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       "      // create a DOM node to render to\n",
       "      const toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
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       "      render(props, toinsert[toinsert.length - 1]);\n",
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       "\n",
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       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
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       "\n",
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       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    const events = require('base/js/events');\n",
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       "\n",
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       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded(error = null) {\n",
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       "          if (error == null) {\n",
       "            return `${prefix} successfully loaded.`;\n",
       "          } else {\n",
       "            return `${prefix} <b>encountered errors</b> while loading and may not function as expected.`;\n",
       "          }\n",
       "        }\n",
       "      })();\n",
       "      el.innerHTML = html;\n",
       "\n",
       "      if (error != null) {\n",
       "        const wrapper = document.createElement(\"div\");\n",
       "        wrapper.style.overflow = \"auto\";\n",
       "        wrapper.style.height = \"5em\";\n",
       "        wrapper.style.resize = \"vertical\";\n",
       "        const content = document.createElement(\"div\");\n",
       "        content.style.fontFamily = \"monospace\";\n",
       "        content.style.whiteSpace = \"pre-wrap\";\n",
       "        content.style.backgroundColor = \"rgb(255, 221, 221)\";\n",
       "        content.textContent = error.stack ?? error.toString();\n",
       "        wrapper.append(content);\n",
       "        el.append(wrapper);\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(() => display_loaded(error), 100);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error(url) {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < css_urls.length; i++) {\n",
       "      const url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < js_urls.length; i++) {\n",
       "      const url = js_urls[i];\n",
       "      const element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.6.2.min.js\"];\n",
       "  const css_urls = [];\n",
       "\n",
       "  const inline_js = [    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "function(Bokeh) {\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      try {\n",
       "            for (let i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "\n",
       "      } catch (error) {display_loaded(error);throw error;\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      const cell = $(document.getElementById(\"c1d9e215-ea64-4085-976e-44c7f1e81bdd\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "'use strict';\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded(error = null) {\n    const el = document.getElementById(\"c1d9e215-ea64-4085-976e-44c7f1e81bdd\");\n    if (el != null) {\n      const html = (() => {\n        if (typeof root.Bokeh === \"undefined\") {\n          if (error == null) {\n            return \"BokehJS is loading ...\";\n          } else {\n            return \"BokehJS failed to load.\";\n          }\n        } else {\n          const prefix = `BokehJS ${root.Bokeh.version}`;\n          if (error == null) {\n            return `${prefix} successfully loaded.`;\n          } else {\n            return `${prefix} <b>encountered errors</b> while loading and may not function as expected.`;\n          }\n        }\n      })();\n      el.innerHTML = html;\n\n      if (error != null) {\n        const wrapper = document.createElement(\"div\");\n        wrapper.style.overflow = \"auto\";\n        wrapper.style.height = \"5em\";\n        wrapper.style.resize = \"vertical\";\n        const content = document.createElement(\"div\");\n        content.style.fontFamily = \"monospace\";\n        content.style.whiteSpace = \"pre-wrap\";\n        content.style.backgroundColor = \"rgb(255, 221, 221)\";\n        content.textContent = error.stack ?? error.toString();\n        wrapper.append(content);\n        el.append(wrapper);\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(() => display_loaded(error), 100);\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.6.2.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n      try {\n            for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n\n      } catch (error) {display_loaded(error);throw error;\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"c1d9e215-ea64-4085-976e-44c7f1e81bdd\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from backtesting.test import SMA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our strategy will be a similar moving average cross-over strategy to the one in\n",
    "[Quick Start User Guide](https://kernc.github.io/backtesting.py/doc/examples/Quick%20Start%20User%20Guide.html),\n",
    "but we will use four moving averages in total:\n",
    "two moving averages whose relationship determines a general trend\n",
    "(we only trade long when the shorter MA is above the longer one, and vice versa),\n",
    "and two moving averages whose cross-over with daily _close_ prices determine the signal to enter or exit the position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from backtesting import Strategy\n",
    "from backtesting.lib import crossover\n",
    "\n",
    "\n",
    "class Sma4Cross(Strategy):\n",
    "    n1 = 50\n",
    "    n2 = 100\n",
    "    n_enter = 20\n",
    "    n_exit = 10\n",
    "    \n",
    "    def init(self):\n",
    "        self.sma1 = self.I(SMA, self.data.Close, self.n1)\n",
    "        self.sma2 = self.I(SMA, self.data.Close, self.n2)\n",
    "        self.sma_enter = self.I(SMA, self.data.Close, self.n_enter)\n",
    "        self.sma_exit = self.I(SMA, self.data.Close, self.n_exit)\n",
    "        \n",
    "    def next(self):\n",
    "        \n",
    "        if not self.position:\n",
    "            \n",
    "            # On upwards trend, if price closes above\n",
    "            # \"entry\" MA, go long\n",
    "            \n",
    "            # Here, even though the operands are arrays, this\n",
    "            # works by implicitly comparing the two last values\n",
    "            if self.sma1 > self.sma2:\n",
    "                if crossover(self.data.Close, self.sma_enter):\n",
    "                    self.buy()\n",
    "                    \n",
    "            # On downwards trend, if price closes below\n",
    "            # \"entry\" MA, go short\n",
    "            \n",
    "            else:\n",
    "                if crossover(self.sma_enter, self.data.Close):\n",
    "                    self.sell()\n",
    "        \n",
    "        # But if we already hold a position and the price\n",
    "        # closes back below (above) \"exit\" MA, close the position\n",
    "        \n",
    "        else:\n",
    "            if (self.position.is_long and\n",
    "                crossover(self.sma_exit, self.data.Close)\n",
    "                or\n",
    "                self.position.is_short and\n",
    "                crossover(self.data.Close, self.sma_exit)):\n",
    "                \n",
    "                self.position.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's not a robust strategy, but we can optimize it.\n",
    "\n",
    "[Grid search](https://en.wikipedia.org/wiki/Hyperparameter_optimization#Grid_search)\n",
    "is an exhaustive search through a set of specified sets of values of hyperparameters. One evaluates the performance for each set of parameters and finally selects the combination that performs best.\n",
    "\n",
    "Let's optimize our strategy on Google stock data using _randomized_ grid search over the parameter space, evaluating at most (approximately) 200 randomly chosen combinations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "76f75e81cb0841a98da56b2a544395d4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Backtest.optimize:   0%|          | 0/9 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 207 ms, sys: 56.6 ms, total: 264 ms\n",
      "Wall time: 3.53 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "from backtesting import Backtest\n",
    "from backtesting.test import GOOG\n",
    "\n",
    "\n",
    "backtest = Backtest(GOOG, Sma4Cross, commission=.002)\n",
    "\n",
    "stats, heatmap = backtest.optimize(\n",
    "    n1=range(10, 110, 10),\n",
    "    n2=range(20, 210, 20),\n",
    "    n_enter=range(15, 35, 5),\n",
    "    n_exit=range(10, 25, 5),\n",
    "    constraint=lambda p: p.n_exit < p.n_enter < p.n1 < p.n2,\n",
    "    maximize='Equity Final [$]',\n",
    "    max_tries=200,\n",
    "    random_state=0,\n",
    "    return_heatmap=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice `return_heatmap=True` parameter passed to\n",
    "[`Backtest.optimize()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.optimize).\n",
    "It makes the function return a heatmap series along with the usual stats of the best run.\n",
    "`heatmap` is a pandas Series indexed with a MultiIndex, a cartesian product of all permissible (tried) parameter values.\n",
    "The series values are from the `maximize=` argument we provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n1   n2   n_enter  n_exit\n",
       "20   60   15       10       10102.867\n",
       "     80   15       10        9864.219\n",
       "     100  15       10       11003.218\n",
       "30   40   20       15       11771.286\n",
       "          25       15       16178.548\n",
       "     60   15       10       11297.192\n",
       "          25       10       12316.818\n",
       "                   20       11474.947\n",
       "     80   15       10        8595.678\n",
       "          25       15       14920.305\n",
       "                               ...   \n",
       "100  160  30       20        9369.124\n",
       "     180  20       15       14260.408\n",
       "          25       10        8592.888\n",
       "                   20        8887.093\n",
       "          30       10        8912.898\n",
       "     200  15       10       13118.248\n",
       "          20       10       11308.462\n",
       "                   15       16350.944\n",
       "          25       10        8991.553\n",
       "          30       10        9953.070\n",
       "Name: Equity Final [$], Length: 177, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This heatmap contains the results of all the runs,\n",
    "making it very easy to obtain parameter combinations for e.g. three best runs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n1   n2   n_enter  n_exit\n",
       "100  120  15       10       18159.064\n",
       "     160  20       15       19216.545\n",
       "50   160  20       15       19565.692\n",
       "Name: Equity Final [$], dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap.sort_values().iloc[-3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we use vision to make judgements on larger data sets much faster.\n",
    "Let's plot the whole heatmap by projecting it on two chosen dimensions.\n",
    "Say we're mostly interested in how parameters `n1` and `n2`, on average, affect the outcome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>n2</th>\n",
       "      <th>40</th>\n",
       "      <th>60</th>\n",
       "      <th>80</th>\n",
       "      <th>100</th>\n",
       "      <th>120</th>\n",
       "      <th>140</th>\n",
       "      <th>160</th>\n",
       "      <th>180</th>\n",
       "      <th>200</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11253.157</td>\n",
       "      <td>7101.260</td>\n",
       "      <td>11323.427</td>\n",
       "      <td>10163.322</td>\n",
       "      <td>11944.455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8958.143</td>\n",
       "      <td>9538.050</td>\n",
       "      <td>9884.416</td>\n",
       "      <td>9685.920</td>\n",
       "      <td>11343.644</td>\n",
       "      <td>8806.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10863.109</td>\n",
       "      <td>7721.244</td>\n",
       "      <td>9139.946</td>\n",
       "      <td>8813.950</td>\n",
       "      <td>10414.656</td>\n",
       "      <td>8908.486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14712.143</td>\n",
       "      <td>7192.893</td>\n",
       "      <td>10403.015</td>\n",
       "      <td>10065.280</td>\n",
       "      <td>8293.734</td>\n",
       "      <td>9895.782</td>\n",
       "      <td>9360.478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9232.415</td>\n",
       "      <td>8046.486</td>\n",
       "      <td>10838.454</td>\n",
       "      <td>12876.589</td>\n",
       "      <td>10312.955</td>\n",
       "      <td>9427.545</td>\n",
       "      <td>9555.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8383.465</td>\n",
       "      <td>10180.503</td>\n",
       "      <td>10563.790</td>\n",
       "      <td>9081.947</td>\n",
       "      <td>14272.266</td>\n",
       "      <td>13575.861</td>\n",
       "      <td>11383.465</td>\n",
       "      <td>10053.469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13666.448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7549.100</td>\n",
       "      <td>10629.479</td>\n",
       "      <td>12860.994</td>\n",
       "      <td>11405.291</td>\n",
       "      <td>10863.807</td>\n",
       "      <td>10658.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>13974.917</td>\n",
       "      <td>11696.319</td>\n",
       "      <td>11757.991</td>\n",
       "      <td>15092.994</td>\n",
       "      <td>13152.243</td>\n",
       "      <td>11518.687</td>\n",
       "      <td>11271.354</td>\n",
       "      <td>11384.551</td>\n",
       "      <td>10649.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10102.867</td>\n",
       "      <td>9864.219</td>\n",
       "      <td>11003.218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "n2        40        60        80        100       120       140       160  \\\n",
       "n1                                                                          \n",
       "100       NaN       NaN       NaN       NaN 11253.157  7101.260 11323.427   \n",
       "90        NaN       NaN       NaN  8958.143  9538.050  9884.416  9685.920   \n",
       "80        NaN       NaN       NaN 10863.109  7721.244  9139.946  8813.950   \n",
       "70        NaN       NaN 14712.143  7192.893 10403.015 10065.280  8293.734   \n",
       "60        NaN       NaN  9232.415  8046.486 10838.454 12876.589 10312.955   \n",
       "50        NaN  8383.465 10180.503 10563.790  9081.947 14272.266 13575.861   \n",
       "40        NaN 13666.448       NaN  7549.100 10629.479 12860.994 11405.291   \n",
       "30  13974.917 11696.319 11757.991 15092.994 13152.243 11518.687 11271.354   \n",
       "20        NaN 10102.867  9864.219 11003.218       NaN       NaN       NaN   \n",
       "\n",
       "n2        180       200  \n",
       "n1                       \n",
       "100 10163.322 11944.455  \n",
       "90  11343.644  8806.572  \n",
       "80  10414.656  8908.486  \n",
       "70   9895.782  9360.478  \n",
       "60   9427.545  9555.402  \n",
       "50  11383.465 10053.469  \n",
       "40  10863.807 10658.140  \n",
       "30  11384.551 10649.053  \n",
       "20        NaN       NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hm = heatmap.groupby(['n1', 'n2']).mean().unstack()\n",
    "hm = hm[::-1]\n",
    "hm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot this table as a heatmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(hm, cmap='viridis')\n",
    "_ = (\n",
    "    ax.set_xticks(range(len(hm.columns)), labels=hm.columns),\n",
    "    ax.set_yticks(range(len(hm)), labels=hm.index),\n",
    "    ax.set_xlabel('n2'),\n",
    "    ax.set_ylabel('n1'),\n",
    "    ax.figure.colorbar(im, ax=ax),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that, on average, we obtain the highest result using trend-determining parameters `n1=30` and `n2=100` or `n1=70` and `n2=80`,\n",
    "and it's not like other nearby combinations work similarly well — for our particular strategy, these combinations really stand out.\n",
    "\n",
    "Since our strategy contains several parameters, we might be interested in other relationships between their values.\n",
    "We can use\n",
    "[`backtesting.lib.plot_heatmaps()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.plot_heatmaps)\n",
    "function to plot interactive heatmaps of all parameter combinations simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"c79192d0-f7fc-437d-9ff7-4fe54136f62b\" data-root-id=\"p1288\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
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       "  const render_items = [{\"docid\":\"321cbe9a-243b-4f14-8958-218d4d993253\",\"roots\":{\"p1288\":\"c79192d0-f7fc-437d-9ff7-4fe54136f62b\"},\"root_ids\":[\"p1288\"]}];\n",
       "  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1288"
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     },
     "output_type": "display_data"
    },
    {
     "data": {
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       "<div style=\"display: table;\"><div style=\"display: table-row;\"><div style=\"display: table-cell;\"><b title=\"bokeh.models.plots.GridPlot\">GridPlot</b>(</div><div style=\"display: table-cell;\">id&nbsp;=&nbsp;'p1288', <span id=\"p1303\" style=\"cursor: pointer;\">&hellip;)</span></div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">align&nbsp;=&nbsp;'auto',</div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">aspect_ratio&nbsp;=&nbsp;None,</div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">children&nbsp;=&nbsp;[(figure(id='p1002', ...), 0, 0), (figure(id='p1048', ...), 0, 1), (figure(id='p1094', ...), 0, 2), (figure(id='p1140', ...), 1, 0), (figure(id='p1186', ...), 1, 1), (figure(id='p1232', ...), 1, 2)],</div></div><div 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none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">rows&nbsp;=&nbsp;None,</div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">sizing_mode&nbsp;=&nbsp;None,</div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">spacing&nbsp;=&nbsp;0,</div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">styles&nbsp;=&nbsp;{},</div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">stylesheets&nbsp;=&nbsp;[],</div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">subscribed_events&nbsp;=&nbsp;PropertyValueSet(),</div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">syncable&nbsp;=&nbsp;True,</div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">tags&nbsp;=&nbsp;[],</div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">toolbar&nbsp;=&nbsp;Toolbar(id='p1287', ...),</div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">toolbar_location&nbsp;=&nbsp;'above',</div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">visible&nbsp;=&nbsp;True,</div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">width&nbsp;=&nbsp;None,</div></div><div class=\"p1302\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">width_policy&nbsp;=&nbsp;'auto')</div></div></div>\n",
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       "    }\n",
       "    ellipsis.innerHTML = expanded ? \"&hellip;)\" : \"&lsaquo;&lsaquo;&lsaquo;\";\n",
       "    expanded = !expanded;\n",
       "  });\n",
       "})();\n",
       "</script>\n"
      ],
      "text/plain": [
       "GridPlot(id='p1288', ...)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from backtesting.lib import plot_heatmaps\n",
    "\n",
    "\n",
    "plot_heatmaps(heatmap, agg='mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model-based optimization\n",
    "\n",
    "Above, we used _randomized grid search_ optimization method. Any kind of grid search, however, might be computationally expensive for large data sets. In the follwing example, we will use\n",
    "[_SAMBO Optimization_](https://sambo-optimization.github.io)\n",
    "package to guide our optimization better informed using forests of decision trees.\n",
    "The hyperparameter model is sequentially improved by evaluating the expensive function (the backtest) at the next best point, thereby hopefully converging to a set of optimal parameters with **as few evaluations as possible**.\n",
    "\n",
    "So, with `method=\"sambo\"`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "! pip install sambo  # This is a run-time dependency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d091822151a54ce581c47d7ded59c125",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Backtest.optimize:   0%|          | 0/40 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%%time\n",
    "\n",
    "stats, heatmap, optimize_result = backtest.optimize(\n",
    "    n1=[10, 100],      # Note: For method=\"sambo\", we\n",
    "    n2=[20, 200],      # only need interval end-points\n",
    "    n_enter=[10, 40],\n",
    "    n_exit=[10, 30],\n",
    "    constraint=lambda p: p.n_exit < p.n_enter < p.n1 < p.n2,\n",
    "    maximize='Equity Final [$]',\n",
    "    method='sambo',\n",
    "    max_tries=40,\n",
    "    random_state=0,\n",
    "    return_heatmap=True,\n",
    "    return_optimization=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n1  n2  n_enter  n_exit\n",
       "63  90  40       30       37171.708\n",
       "54  84  38       25       41224.827\n",
       "57  87  40       30       54980.767\n",
       "Name: Equity Final [$], dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap.sort_values().iloc[-3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the optimization runs somewhat slower even though `max_tries=` is lower. This is due to the sequential nature of the algorithm and should actually perform quite comparably even in cases of _much larger parameter spaces_ where grid search would effectively blow up, likely reaching a better optimum than a simple randomized search would.\n",
    "A note of warning, again, to take steps to avoid\n",
    "[overfitting](https://en.wikipedia.org/wiki/Overfitting)\n",
    "insofar as possible.\n",
    "\n",
    "Understanding the impact of each parameter on the computed objective function is easy in two dimensions, but as the number of dimensions grows, partial dependency plots are increasingly useful.\n",
    "[Plotting tools from _SAMBO_](https://sambo-optimization.github.io/doc/sambo/plot.html)\n",
    "take care of the more mundane things needed to make good and informative plots of the parameter space.\n",
    "\n",
    "Note, because SAMBO internally only does _minimization_, the values in `optimize_result` are negated (less is better)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 680x705 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sambo.plot import plot_objective\n",
    "\n",
    "names = ['n1', 'n2', 'n_enter', 'n_exit']\n",
    "_ = plot_objective(optimize_result, names=names, estimator='et')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 680x705 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sambo.plot import plot_evaluations\n",
    "\n",
    "_ = plot_evaluations(optimize_result, names=names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn more by exploring further\n",
    "[examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials)\n",
    "or find more framework options in the\n",
    "[full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules)."
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "encoding": "# -*- coding: utf-8 -*-"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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